Fig. 1. Simulated speckle images

Fig. 2. Convergence frequency of IC-LM algorithm with different damping coefficients. (a) No noise, u=v=1 pixel; (b) no noise, u=v=4 pixel; (c) no noise, u=v=7 pixel; (d) Gaussian noise, u=v=1 pixel; (e) Gaussian noise, u=v=4 pixel; (f) Gaussian noise, u=v=7 pixel

Fig. 3. Convergence frequency of IC-LM2 algorithm with different damping coefficients. (a) No noise, u=v=1 pixel; (b) no noise, u=v=4 pixel; (c) no noise, u=v=7 pixel; (d) Gaussian noise, u=v=1 pixel; (e) Gaussian noise, u=v=4 pixel; (f) Gaussian noise, u=v=7 pixel

Fig. 4. Comparison of convergence speed of reverse combination and forward combination algorithms. (a1)(b1) u=v=1 pixel; (a2)(b2) u=v=4 pixel; (a3)(b3) u=v=7 pixel

Fig. 5. Comparison of convergence frequency of inverse combination algorithms. (a) Subset size: 21 pixel×21 pixel; (b) subset size: 31 pixel×31 pixel; (c) subset size: 41 pixel×41 pixel; (d) subset size: 51 pixel×51 pixel; (e) subset size: 61 pixel×61 pixel; (f) subset size: 71 pixel×71 pixel

Fig. 6. Comparison of convergence frequency of inverse combination algorithm under Gaussian noise. (a) Subset size: 21 pixel×21 pixel; (b) subset size: 31 pixel×31 pixel; (c) subset size: 41 pixel×41 pixel; (d) subset size: 51 pixel×51 pixel; (e) subset size: 61 pixel×61 pixel; (f) subset size: 71 pixel×71 pixel

Fig. 7. Comparison of computational speed of reverse combination algorithm. (a) Subset size: 21 pixel×21 pixel; (b) subset size: 31 pixel×31 pixel; (c) subset size: 41 pixel×41 pixel; (d) subset size: 51 pixel×51 pixel; (e) subset size: 61 pixel×61 pixel; (f) subset size: 71 pixel×71 pixel

Fig. 8. Comparison of average iteration times of reverse combination algorithm under Gaussian noise. (a) Subset size: 21 pixel×21 pixel; (b) subset size: 31 pixel×31 pixel; (c) subset size: 41 pixel×41 pixel; (d) subset size: 51 pixel×51 pixel; (e) subset size: 61 pixel×61 pixel; (f) subset size: 71 pixel×71 pixel

Fig. 9. Speckle images under different noise

Fig. 10. Comparison of sub-pixel displacement calculation accuracy of reverse combination algorithm. (a) No noise; (b) Gaussian noise (S_td=0.02); (c) Gaussian noise (S_td=0.04); (d) Gaussian noise (S_td=0.06); (e) Gaussian noise (S_td=0.08); (f) Gaussian noise (S_td=0.10)

Fig. 11. Star displacement field. (a) Speckle image; (b) displacement field v in y-direction

Fig. 12. Displacement and error under different subregions. (a) Displacement; (b) error

Fig. 13. Displacements and errors calculated by IC-Diag and IC-Diag2. (a) IC-Diag; (b) IC-Diag2

Fig. 14. Displacements and errors calculated by IC-LM and IC-LM2. (a) IC-LM; (b) IC-LM2

Fig. 15. Displacements and errors calculated by IC-DogLeg and IC-DogLeg2. (a) IC-DogLeg; (b) IC-DogLeg2

Fig. 16. Displacements and errors calculated by IC-GN and IC-GN2. (a) IC-GN; (b) IC-GN2

Fig. 17. Displacement and error of reverse combination algorithm along central axis Δ direction. (a) Displacement; (b) error

Fig. 18. Displacement and error of inverse combination algorithm along central axis Δ direction under noise. (a) Displacement; (b) error

Fig. 19. Experimental device

Fig. 20. Compression deformation images and displacement fields v of rubber block

Fig. 21. Displacement and strain fields of the last frame of rubber block. (a) Displacement u; (b) displacement v; (c) strain εxx; (d) strain εyy; (e) strain γxy